Tag Archives: hedge funds

Our earlier work showed that simple performance metrics, such as nominal returns and Sharpe Ratios, revert. Because of this reversion, above-average past performers tend to become below-average and vice versa. This reversion is primarily due to systematic (factor) noise. Consequently, metrics that remove factor effects from performance reveal persistent stock picking skill. Prompted by readers’ questions, we have investigated the predictive power of popular performance metrics. This article reviews the predictive power of information ratios. They offer a large improvement over simple nominal returns, naive alphas, and Sharpe ratios, but still fall short of the most predictive metrics. Over a 3-year window, the predictive power of information ratios for skill evaluation and manager selection is approximately half that of security selection distilled with a statistical equity risk model.

Measuring the Predictive Power of Information Ratios

We analyze portfolios of all institutions that have filed Forms 13F in the past 15 years. This survivorship-free portfolio dataset covers firms that have held as least $100 million in long U.S. assets. Approximately 5,000 portfolios had sufficiently long histories and low turnover to be analyzable.

To measure the persistence of performance metrics over time, we compare metrics measured in two 12-month periods separated by variable delay. One example is the 24-month delay that separates metrics for 1/31/2010-1/31/2011 and 1/31/2013-1/31/2014. A 24-month delay of 12-month metrics thus covers a 48-month time window. We use Spearman’s rank correlation coefficient to calculate statistically robust correlations.

Serial Correlation of Information Ratios

The information ratio is similar to the Sharpe ratio, but with a key upgrade: Sharpe ratio evaluates returns relative to the risk-free rate. Information ratio evaluates returns relative to a (presumably appropriate) benchmark. We use the S&P 500 Index as the benchmark, following a common practice. As a benchmark increasingly matches the factor exposures of a portfolio, information ratios converge to the standard score (z-score) of active returns estimated with a capable equity risk model. Due to the more effective handling of systematic risk, the predictive power of information ratios receives a boost.

The chart below shows correlation between 12-month Information Ratios calculated with lags of one to sixty months (1-60 month delay):

13F Equity Portfolios: Serial correlation of Information Ratios

Delay (months)

Serial Correlation

1

0.06

6

0.05

12

0.03

18

0.05

24

0.06

30

0.06

36

0.02

42

-0.02

48

-0.06

54

-0.04

60

0.02

Over the 3-year window, the serial correlation (autocorrelation) of Information Ratios is approximately half of the serial correlation of security selection returns provided in the following section. Unlike simple nominal returns and Sharpe ratios, information ratios do not suffer from short-term reversion.

Serial Correlation of Nominal Returns

For comparison, the following chart shows serial correlation of 12-month cumulative nominal returns calculated with 1-60 month lags. As we discussed in prior articles, these revert with an approximately 18-month cycle – so strong past nominal returns are actually predictive of poor short-term future nominal returns:

13F Equity Portfolios: Serial correlation of nominal returns

Delay (months)

Serial Correlation

1

-0.14

6

-0.24

12

-0.33

18

-0.06

24

0.16

30

0.23

36

0.08

42

-0.26

48

-0.44

54

-0.23

60

0.13

Serial Correlation of Security Selection Returns

As we mentioned above, when a benchmark’s factor exposures match those of the portfolio, information ratio is equivalent to the standard score (z-score) of active returns estimated with a capable equity risk model. In practice, however, information ratio is typically calculated relative to a broad benchmark, such as the S&P 500 Index for equity portfolios. Consequently, one would expect the predictive power of information ratios to be lower than the predictive power of security selection returns, properly estimated. For comparison, we provide serial correlation of a security selection metric that uses an equity risk model to control for factor exposures.

To eliminate the disruptive factor effects responsible for performance reversion, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection net of factor effects. αReturn is the return a portfolio would have generated if all factor returns had been zero. The following chart shows correlation between 12-month cumulative αReturns calculated with 1-60 month lags:

The predictive power of αReturns, as measured by their serial correlation of 12-month performance metrics, is approximately twice that of information ratios over a 3-year window (12-month delay between 12-month performance metrics), but the two begin to converge after three years.

For all performance metrics, the above data is aggregate, spanning thousands of portfolios and return windows. Individual firms can overcome the averages; however, the exceptions require especially careful monitoring.

Summary

The predictive power of information ratios is significantly higher than that of nominal returns and Sharpe ratios.

As a benchmark converges to the factor exposures of a portfolio, information ratios converge to the standard score (z-score) of active returns estimated with a capable risk model.

Over a 3-year window, the predictive power of information ratios, as commonly calculated, is approximately half that of the security selection return calculated with a predictive equity risk model.

Measuring the Predictive Power of Sharpe Ratios

We analyze portfolios of all institutions that have filed Forms 13F in the past 15 years. This survivorship-free portfolio dataset covers firms that have held as least $100 million in long U.S. assets. Approximately 5,000 portfolios had sufficiently long histories, low turnover, and broad holdings to be analyzable.

To measure the decay of performance metrics over time, we compare metrics measured in two 12-month periods separated by variable delay. One example of 24-month delay is metrics for 1/31/2010-1/31/2011 and 1/31/2013-1/31/2014. We use Spearman’s rank correlation coefficient to calculate statistically robust correlations.

Serial Correlation of Sharpe Ratios

Sharpe Ratio is perhaps the most common performance metric. Since it does not directly control for systematic (factor) portfolio exposures, one would expect this approach to suffer from similar reversion as nominal returns. Indeed, tests reveal that Sharpe Ratios fail to isolate security selection performance: Sharpe Ratios of portfolios revert when factor regimes change. Thus, former leaders tend to become laggards, and former laggards tend to become leaders.

The serial correlation (autocorrelation) of Sharpe Ratios is similar to the serial correlation of nominal returns in the next section. The following chart shows correlation between 12-month Sharpe Ratios calculated with lags of one to sixty months (1-60 month lag):

13F Equity Portfolios: Serial correlation of Sharpe Ratios

Delay (months)

Serial Correlation

1

-0.09

6

-0.20

12

-0.28

18

-0.06

24

0.12

30

0.15

36

-0.08

42

-0.40

48

-0.49

54

-0.24

60

0.12

Sharpe Ratios revert with an approximately 18-month cycle. Historical Sharpe Ratios thus have some predictive value, but a negative one. There is a narrow window at 2-3 year lag when past Sharpe Ratios are positively predictive of the future Sharpe Ratios. This is due to the approximately 18-month cycle of reversion.

Serial Correlation of Nominal Returns

For comparison, the following chart shows serial correlations between 12-month cumulative nominal returns calculated with 1-60 month lags. The relationship is similar to that of the Sharpe Ratios. Strong past (nominal) returns are predictive of poor short-term future returns:

13F Equity Portfolios: Serial correlation of nominal returns

Delay (months)

Serial Correlation

1

-0.14

6

-0.24

12

-0.33

18

-0.06

24

0.16

30

0.23

36

0.08

42

-0.26

48

-0.44

54

-0.23

60

0.13

Serial Correlation of Security Selection Returns

For additional comparison, we provide serial correlation of a security selection metric that adjusts for factor exposures. To eliminate the disruptive factor effects responsible for performance reversion, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection net of factor effects. αReturn is the return a portfolio would have generated if all factor returns had been flat. Firms with above-average αReturns in one period are likely to maintain them, though with a decay. The following chart shows correlation between 12-month cumulative αReturns calculated with 1-60 month lags:

Though the above serial correlations of αReturn may appear low, they are amplified and compounded in practical portfolios of multiple funds. A hedged portfolio of the net consensus longs (relative overweights) of the top 5% long U.S. equity stock pickers delivered approximately 8% return independently of the market. The above data is aggregate. Specific outstanding disciplined firms can overcome performance reversion, but they are the exceptions that require careful monitoring.

Summary

Sharpe Ratios revert rapidly and are not significantly better predictors of future performance than nominal returns.

Once performance is controlled for systematic (factor) exposures, security selection returns persist for approximately 5 years.

Selection of superior future performers is possible, but it requires abandoning popular non-predictive metrics and spotting skill long before it is plainly visible and arbitraged away.

Our earlier piece tested several equity market hedging techniques on U.S. equity mutual fund portfolios. We now extend the tests to U.S. hedge fund long equity portfolios. Since these are generally less diversified and more active than mutual funds, simplistic approaches that use a fixed 100% short (1 beta) or rely on returns-based style analysis (RBSA) fail even more dramatically for hedge funds. Yet, a robust statistical equity risk model applied to portfolio holdings remains close to the ideal of perfect hedging. A robust and well-tested technique is thus even more vital for managing hedge fund exposures.

Equity Market Hedging Techniques

We analyze approximately 600 hedge fund long U.S. equity portfolios that are tractable from regulatory filings. Note that roughly half of U.S. hedge fund portfolios are impossible to analyze accurately due to the quarterly data frequency and high turnover. Similarly to our earlier analysis of mutual fund portfolio hedging, we evaluate three approaches to calculating market hedge ratios:

Constant 100% market exposure (1 beta): This common ad-hoc approach used by portfolio managers and analytics vendors supposes that all portfolios have the same risk as a benchmark or a hedge.

Our study spans 10 years. We calculate hedge ratios at the end of each month and use these to hedge portfolios during the following month. This produces a series of 10-year realized (ex-post) hedged portfolio returns. We further break these series into 12-month intervals and calculate their correlations to the Market. Low average market correlation and low dispersion of correlations indicates that a hedging technique effectively eliminates systematic market exposure of a typical portfolio.

Realized Market Correlations of Hedged Hedge Fund Portfolios

Realized Market Correlations of Random Return Series

A large return dataset, even when perfectly random, will contain some subsets with high market correlations. To control for this, we generate random return samples (observations) and calculate their market correlations. These results, attainable only with a perfect hedge, are the standard against which we evaluate equity market hedging techniques:

A 100% hedge is too small for high-risk portfolios and too large for low-risk ones. Also as seen for mutual funds, some hedged low-exposure portfolios formed a fat tail of nearly -1 realized market correlations.

Most RBSA assumes that portfolio factor exposures are constant over the regression window. Some advanced techniques may allow for random variation in exposures over the window, yet even this relaxed assumption is flawed. Our earlier posts covered the problems that arise when RBSA fails to detect rapid changes in portfolio risk. It turns out that the months or years of delay before RBSA captures changes in factor exposures are especially damaging when analyzing hedge fund portfolios:

RBSA fails more severely for hedge funds than for mutual funds. In fact, RBSA has similar defect as a fixed 100% hedge for some low-exposure portfolios and produces a fat tail of nearly -1 market correlations. Hedge funds’ long equity portfolios can and do cut risk rapidly, so RBSA’s failure to detect these rapid exposure reductions is expected.

The edge comes from the analysis of individual positions that responds rapidly to portfolio changes and the robust regression methods that are resilient to outliers. The result is superior analysis of individual funds.

Whereas tests using hedge fund long equity portfolios accentuate the flaws of simple hedging and returns-based analysis, the AlphaBetaWorks Statistical Equity Risk Model remains close to the baseline of a perfect hedge. Thus, it is even more vital that portfolio managers and investors who analyze or manage hedge fund equity risk rely on robust models and thoroughly tested methods.

Summary

Random portfolio returns that would be produced by a perfect hedge are the standard to which equity market hedging techniques can be compared.

Simplistic hedging that assumes 1 beta for all hedge fund long equity portfolios over-hedges some and under-hedges others, resulting in hedged portfolios with net short and net long realized exposures, respectively.

And why Poor Nominal Returns are a Reason to Hire Rather than Fire a Manager

Our earlier pieces discussed how nominal investment performance reverts. Since returns are dominated by systematic risk factors (primarily the Market), they are subject to reversal when investment regimes change. In the simplest terms, high risk funds do well in bull markets, and low risk funds do well in bear markets, irrespectively of stock picking skill. When the tide turns, so does the funds’ relative performance. The persistence of stock picking skill becomes evident once systematic effects are removed.

Measuring Persistence of Investment Performance

As our prior performance persistence work, this study analyzes portfolios of all institutions that have filed Form 13F during the past 15 years. This survivorship-free portfolio database covers thousands of firms that have held at least $100 million in U.S. long assets during this period.

The relationship between performance metrics of a portfolio calculated at different points in time captures their persistence. To measure the persistence of nominal returns, we analyze nominal returns during two 12-month periods separated by variable delay. For example, analysis of 24-month delay includes periods 1/31/2010-1/31/2011 and 1/31/2013-1/31/2014. We use the Spearman’s rank correlation coefficient to calculate statistically robust correlations between metrics. Technically speaking, we are studying the metrics’ serial correlation or autocorrelation.

The Persistence of Investment Performance

Serial Correlation of Nominal Returns

Portfolios with above-average nominal returns for prior 12 months tend to underperform for approximately the following two years; similarly, those with below average nominal returns tend to then outperform:

13F Equity Portfolios: Serial correlation of nominal returns

Delay (months)

Serial Correlation

1

-0.11

6

-0.26

12

-0.36

18

-0.09

24

0.15

30

0.22

36

0.08

42

-0.26

48

-0.42

54

-0.22

60

0.17

Serial Correlation of Security Selection Returns

To eliminate the disruptive factor effects responsible for the above reversion, the AlphaBetaWorks Performance Analytics Platform calculates return from security selection after controlling for the factor exposures. The resulting metric, αReturn, is the return a portfolio would have generated if all factor returns had been flat. Above-average and below-average 12-month αReturns tend to persist for approximately four years:

Delay (months)

Serial Correlation

1

0.08

6

0.10

12

0.08

18

0.05

24

0.05

30

0.04

36

-0.01

42

-0.03

48

-0.04

54

-0.03

60

-0.01

The Persistence of Negative Investment Performance

The autocorrelation of overall nominal returns and αReturns captures the persistence of both negative and positive investment performance, but positive and negative metrics need not have similar persistence. In fact, the problems with nominal returns and simplistic performance metrics derived from them are accentuated when the nominal returns are negative.

Serial Correlation of Negative Nominal Returns

Negative 12-month nominal returns revert even more rapidly and more strongly than overall returns. Rank correlation coefficient for 12-month nominal returns separated by 6 months is approximately -0.5 for negative nominal returns and -0.2 for overall nominal returns. Poor recent nominal returns are a reason to hire rather than fire a manager, at least in the short term (the subsequent 12-18 months):

13F Equity Portfolios: Serial correlation of negative nominal returns

Delay (months)

Serial Correlation

1

-0.57

6

-0.23

12

-0.01

18

-0.14

24

0.52

30

0.18

36

-0.27

42

-0.27

48

-0.24

54

-0.12

60

0.07

Serial Correlation of Negative Security Selection Returns

This reversion is not present for αReturns. Negative αReturns have similar autocorrelation for the first few years and decay more slowly than overall αReturns:

The decay in security selection performance is typically due to such things as talent turnover, style drift, management distraction, and asset growth. Since these are more likely to affect the top-performing funds, negative αReturn remains predictive for longer. The above data is aggregate and specific firms can and do overcome the average fate. Though the above serial correlations may appear low, they are amplified and compounded in portfolios of multiple funds.

Cheerful consensus is usually a recipe for mediocrity, whether investing in a stock or in a fund. Fear and panic in the face of nominal underperformance are more dangerous still. Just as it pays to be a contrarian stock picker, it pays to be a contrarian fund investor or allocator.

Measuring the Decay of Stock Picking Skill

This study analyzes portfolios of all institutions that have filed Form 13F. This is the broadest and most representative survivorship-free portfolio database covering thousands of firms that hold at least $100 million or more in U.S. long assets. Approximately 5,000 firms had sufficiently long histories, low turnover, and broad portfolios suitable for skill evaluation.

To measure the decay of stock picking performance over time, we compare metrics measured in two 12-month periods separated by variable delay. One example of 24-month delay is metrics for 1/31/2010-1/31/2011 and 1/31/2013-1/31/2014. We use Spearman’s rank correlation coefficient to calculate statistically robust correlations.

Serial Correlation of Nominal Returns

The following chart shows serial correlation (autocorrelation) between 12-month cumulative nominal returns calculated with lags of one to sixty months (1-60 month lag). The relationship is generally negative. This illustrates that strong past (nominal) returns are predictive of future returns, albeit poor in the short-term:

13F Equity Portfolios: Serial correlation of nominal returns

Delay (months)

Serial Correlation

1

-0.11

6

-0.26

12

-0.36

18

-0.09

24

0.15

30

0.22

36

0.08

42

-0.26

48

-0.42

54

-0.22

60

0.17

There is a narrow window at 2-3 year lag when past returns are predictive of the future results. This appears to be due to the approximately 18-month cycle of reversion in 12-month nominal performance.

Serial Correlation of Naive Alphas

It is common to measure alpha simply as outperformance relative to a benchmark. We will call this approach “naive alpha.” Since it ignores portfolio risk, this approach does not eliminate systematic (factor) effects and fails to isolate security selection performance: The top nominal performers who took the most systematic risk in a bullish regime remain the top performers after a benchmark return is subtracted. When regimes change, these former leaders tend to become the laggards, and vice versa.

Indeed, the serial correlation of naive alphas is similar to the serial correlation of nominal returns. The following chart shows correlation between 12-month cumulative naive alphas calculated with 1-60 month lags:

Serial Correlation of Security Selection Returns

To eliminate the disruptive factor effects responsible for performance reversion, the AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection net of factor effects. αReturn is the return a portfolio would have generated if all factor returns had been flat.

Firms with above-average αReturns in one period are likely to maintain them in the other, but with decay. The following chart shows correlation between 12-month cumulative αReturns calculated with 1-60 month lags:

Though the above serial correlations of αReturn may appear low, they are amplified and compounded in practical portfolios of multiple funds. A hedged portfolio of the net consensus longs (relative overweights) of the top 5% long U.S. equity stock pickers delivered approximately 8% return independently of the market.

For approximately 3 years, strong security selection performance, as measured by the 12-month αReturn, is predictive of the future 12-month results. Returns due to security selection thus persist for approximately 5 years. This means that as little as 12 month of consistently positive αReturns are a positive indicator for the following four years. Skilled stock pickers can be spotted years before their skill is plainly visible and broadly exploited.

The decay in security selection performance is typically due to the following sources: talent turnover, style drift, management distraction, and asset growth. It is not a coincidence that the conventional requirement for large institutional allocation is 3-5 year track record.

The above data is aggregate. Specific outstanding disciplined firms can overcome this reversion, but they are the exceptions that require careful monitoring. Spotting skilled managers before their skill is visible to all is a sounder path to superior selection. In this respect, investing with managers is very similar to investing in stocks. Manager skill is arbitraged away – analytical advantage over the crowd is key. Cheerful consensus is usually a recipe for mediocrity whether investing in a stock or in a fund.

Performance of the Top U.S. Stock Pickers

Since genuine investment skill persists, top U.S. stock pickers tend to generate persistently positive returns from security selection (idiosyncratic, residual returns). This strong performance derives from the top stock pickers’ individual positions. Their consensus exposures thus cut through the fog of panic and confusion, such as that surrounding VRX.

We track institutional ownership of Valeant by the AlphaBetaWorks Expert Aggregate (ABW Expert Aggregate). The ABW Expert Aggregate is sourced from all institutions that have filed Form 13F.

Nominal returns and related simplistic metrics of investment skill (Sharpe Ratio, Win/Loss Ratio, etc.) are dominated by systematic factors and thus revert. So for an accurate assessment of manager skill, we must eliminate the systematic effects and estimate residual performance due to stock picking. The AlphaBetaWorks Performance Analytics Platform calculates each portfolio’s return from security selection – αReturn. αReturn is the performance a portfolio would have generated if all factor returns had been flat. Each month we identify the five percent of 13F-filers with the most consistently positive αReturns over the prior 36 months. This expert panel of the top stock pickers typically consists of 100-150 firms.

The top panel on the following chart shows the performance of VRX. Nominal returns are in black and cumulative residual returns (αReturns) are in blue. αReturn is the performance VRX would have generated if all systematic factor returns had been flat. The bottom panel shows exposure to VRX within the ABW Expert Aggregate:

VRX: Cumulative αReturns and Exposure within the ABW Expert Aggregate

Top stock pickers had negligible exposure to VRX until 2011. In early-2011 smart-money exposure to VRX grew rapidly, peaking between 2012 and 2014. Throughout 2014, as the shares appreciated, the top U.S. stock pickers sharply cut their exposure to VRX.

The panic hit a year after the top stock pickers dramatically cut their exposure. By 2015 smart-money exposure to VRX was a mixed signal as it tracked the stock’s volatility. Ironically, this is when VRX became one of the most crowded hedge fund bets. Though VRX had a history of smart-money ownership, this vote of confidence was withdrawn by 2015. Future stock-specific returns to VRX, as predicted by the ABW Expert Aggregate, increased rapidly in 2011 and dropped rapidly by 2015.

Investors, allocators, and fund-followers, armed with our analytics, would have taken note. Those looking for the smart money to back their long or short views of VRX must now look elsewhere.

Hedge fund replication, tracking, and analysis often focus on the individual positions and on the stock-specific risk they contribute. This overlooks the far more important return source – systematic hedge fund volatility. A statistical equity risk model that robustly captures exposures to the key systematic risk factors delivers 0.92 (92%) correlation between predicted and actual returns for most hedge fund long equity portfolios reported in regulatory filings. It follows that risk, performance, and crowding of these portfolios are overwhelmingly systematic. These systematic returns can be replicated and hedged with models built on investable factors and with passive investments that track these factors. Fixation on individual holdings and position overlap is misguided at best and dangerous at worst.

U.S. Hedge Fund Equity Portfolio Sample

Our analysis covers approximately 300 U.S. hedge fund long equity portfolios active over the past 15 years. We started with the universe of over 1,000 funds. First, we eliminated half that were not analyzable from regulatory filings (primarily because of high turnover). We then eliminated the portfolios that had less than five years of contiguous history or fewer than five equity positions during a period.

Our conclusions apply to most hedge fund long equity portfolios, though high turnover funds will require higher frequency of position data for predictive return forecasts. By extension, these findings also apply to long-biased hedge funds that derive most of their performance from their long portfolios. They apply to short books as well: our experience shows that short hedge fund books tend to be even more diversified and hence even more governed by their systematic exposures (possibly due to managers’ sensitivity to stock-specific risk). Finally, systematic volatility is even higher for funds of hedge funds due to their higher diversification.

Testing the Predictive Power of Equity Risk Models for Hedge Funds

To quantify the share of systematic hedge fund volatility, we need to test the ability of an equity risk model to predict future hedge fund returns. We followed the approach of our earlier studies of equity risk models’ predictive power:

Calculated factor exposures from the estimated holdings at the end of each month and the holdings’ factor exposures;

Predicted the following month’s returns using these ex-ante factor exposures and ex-post factor returns;

Compared returns predicted by past factor exposures to subsequent portfolio performance and evaluated the predictive accuracy of a model.

The correlation between predicted and actual returns measures a model’s accuracy and determines the fraction of systematic hedge fund volatility as defined by the model. The higher the correlation, the more effective a model is at hedging, stress testing, replication, and evaluating investmentskill.

For the 25% of hedge fund portfolios it handles the worst, the model still achieves 0.51-0.86 correlation between predicted monthly returns and realized monthly returns.

Many of the outliers with low correlations hold substantial fixed income, exotic, and hybrid positions. To provide the most conservative assessment of the model’s predictive accuracy and the proportion of systematic risk, we did not alter the universe to exclude these outliers.

Unsurprisingly, given hedge funds’ aggressiveness, the model is less predictive for hedge funds than they are for mutual funds and insurance companies. Still, 85% of monthly return variance of most hedge fund long equity portfolios is systematic. And over 80% of that is due to a single factor. In other words, investors could replicate over 80% of this variance simply by replicating the hedge funds’ Market exposure.

Amazingly, much of the popular analysis of hedge fund risk and crowding ignores systemic factors and focuses solely on individual positions. This simplistic approach, even in the rare cases where it is done correctly, addresses only 15% of the risk that is residual, idiosyncratic, or stock-specific. Investors who rely on these simplistic approaches will make a variety of mistakes. For instance, they might wrongly conclude that two funds are differentiated because they have no positions in common. Yet, these two funds can have similar systematic factor exposures and thus high correlation; market hiccups will be magnified.

Conclusions

For most U.S. hedge fund long equity portfolios estimated from regulatory filings, a robust statistical equity risk model delivers over 0.92 correlation between predicted monthly returns and realized monthly returns.

85% of the monthly return variance of hedge fund long equity portfolios is systematic, hence not attributable to individual stock bets.

Fixation on hedge funds’ stock-specific bets and crowding is misguided and dangerous, since this risk accounts for at most 15% of the portfolio variance for most hedge funds.

Investors who rely on simplistic approaches to hedge fund risk analysis may make a variety of mistakes, including missed crowding due to shared systematic factor exposures.

In an earlier post, we discussed the largest bets hedge fund long portfolios were making in Q1 2015. The third largest was on the Industrials Factor. This is the risk specific to the industrials sector after controlling for market exposure. It captures capital allocation to industrials and sensitivity (beta) to the sector. There is weak statistical evidence of poor industrials factor timing by hedge funds – investors who follow hedge funds should either ignore this bet or treat it as a negative indicator.

At the end of Q1 2015, high industrials sector factor exposure was the third largest source of U.S. hedge funds’ long portfolio crowding. HF Aggregate, a portfolio consisting of popular long U.S. equity holdings of all hedge funds tractable from quarterly filings, had over 25% industrials factor exposure – a 9% overweight relative to Market. This exposure was at an all-time high:

This industrials factor exposure captures sector risk after controlling for market exposure. For example, a fund with 10% allocated to a broad industrials index will have approximately 10% industrials factor exposure. A fund with 10% allocated to a 2x-levered broad industrials ETF will have approximately 20% industrials factor exposure.

Here we analyze the hedge fund industry’s skill in timing the U.S. Industrials Factor by varying this exposure. The AlphaBetaWorks Performance Analytics Platform evaluates market timing skills and performance using two related tests:

Statistical test for the relationship between factor exposure and subsequent factor returns,

Statistical test for the size and consistency of returns generated by varying factor exposures.

Hedge Fund Industrials Factor Exposure and Industrials Factor Return

We calculated the Spearman’s rank correlation coefficient of HF Aggregate’s industrials factor exposure and subsequent industrials factor return for the past 10 years and tested it for significance. The chart below illustrates the correlation between the two series and the test results:

Hedge Fund Industrials Factor Timing Returns

Over the past 10 years, HF Aggregate (USHFS in red) made approximately 0.9% less than it would have with constant industrials factor exposure, as illustrated below. The performance of HF Aggregate is compared to all tractable 13F filers (Group in gray). The AlphaBetaWorks Performance Analytics Platform identifies this performance due to industrials factor timing as industrials βReturn:

U.S. Hedge Fund Aggregate’s Industrials Factor Timing Return

The weak evidence of poor industrials factor timing by the industry, combined with the high recent industrials factor exposure, is a weak bearish indicator for the sector.

Summary

The industrials factor exposure of U.S. hedge funds’ long portfolios is weakly predictive of subsequent sector performance.

Current hedge fund industrials factor exposure, at 10-year highs, is a weak bearish indicator for the Industrials Sector.

Investors who track hedge fund holdings should either ignore this bet or treat is as a negative indicator.

In an earlier post, we discussed the largest bets hedge fund long portfolios were making in Q1 2015. The second largest was on the oil price. This exposure is the residual oil price risk after controlling for market and sector exposures. It captures overweighting within various sectors of companies that out- or underperform under rising oil price. There is weak statistical evidence of skilled oil factor timing by hedge funds – the current bet is a weak bullish indicator for oil prices.

At the end of Q1 2015, high oil price factor exposure was the second largest source of U.S. hedge funds’ long portfolio crowding. HF Aggregate, a portfolio consisting of popular long U.S. equity holdings of all hedge funds tractable from quarterly filings, had approximately 2.5% oil price factor exposure. This exposure was approaching the 10-year highs reached in 2007-2009:

Statistical test for the relationship between factor exposure and subsequent factor returns,

Statistical test for the size and consistency of returns generated by varying factor exposures.

Hedge Fund Oil Factor Exposure and Oil Return

We calculated the Spearman’s rank correlation coefficient of HF Aggregate’s oil price factor exposure and subsequent oil price return and tested it for significance. The chart below illustrates the correlation between the two series and the test results:

Hedge Fund Oil Factor Timing Returns

Over the past 10 years, HF Aggregate (USHFS in red) made approximately 1.5% more than it would have with constant oil factor exposure, as illustrated below. The performance of HF Aggregate is compared to all tractable 13F filers (Group in gray). The AlphaBetaWorks Performance Analytics Platform identifies this performance due to oil price factor timing as oil price βReturn:

U.S. Hedge Fund Aggregate’s Oil Factor Timing Return

The weak evidence of oil factor timing skill by the industry, combined with the high recent oil factor exposure, is a weak bullish indicator for oil prices.

Summary

The oil price factor exposure of U.S. hedge funds’ long portfolios is weakly predictive of subsequent oil performance.

The single largest bet hedge fund long portfolios were making in Q1 2015 was on the U.S. market (high beta). While the bet is exceptionally large, it is not predictive of market direction. Allocators should pay attention to this risk aggregation.

At the end of Q1 2015, high market factor exposure (high beta) was the primary source of U.S. hedge funds’ long portfolio crowding. HF Aggregate, a portfolio consisting of popular long U.S. equity holdings of all hedge funds tractable from quarterly filings, had approximately 115% U.S. market factor exposure (1.15 U.S. market beta). This exposure was at 10-year highs – the level last seen in mid-2006:

U.S. Hedge Fund Aggregate’s U.S. Market Factor Exposure History

Our work on hedge fund crowding has so far not addressed hedge fund factor timing and hedge fund U.S. market timing specifically. We dive into this performance here.

Statistical test for the relationship between factor exposure and subsequent factor returns,

Statistical test for the size and consistency of returns generated by varying factor exposures.

Hedge Fund Market Exposure and Market Return

We calculated the Spearman’s rank correlation coefficient of HF Aggregate’s market exposure and subsequent market return and tested it for significance. The chart below illustrates the correlation between the two series and the test results:

U.S. Hedge Fund Aggregate’s U.S. Market Exposure and Return

The chart shows that there is a positive relationship between HF Aggregate’s U.S. market exposure and subsequent U.S. market return, but it is weak and statistically insignificant. In other words, hedge funds’ variation in U.S. market exposure has done little to help their performance.

Hedge Fund U.S. Market Timing Returns

Over the past 10 years, HF Aggregate (USHFS in red) made approximately 1% more than it would have with constant factor exposures, as illustrated below. The performance of HF Aggregate is compared to all tractable 13F filers (Group in gray). The AlphaBetaWorks Performance Analytics Platform identifies this performance due to U.S. market factor timing as U.S. market βReturn:

U.S. Hedge Fund Aggregate’s U.S. Market Timing Return

The performance impact of this variation in beta within U.S. hedge fund long portfolios is also minor. However, the group of all 13F filers was a poor market timer, particularly during the volatility of 2008-2009. During the crisis, non-filers were the smart money and took advantage of U.S. market volatility, at the expense of 13F filers. 13F filers were a contrarian indicator.

Summary

Hedge fund long equity portfolios consistently take 5-15% more market risk than S&P500 and other broad market benchmarks.

There has been no statistically significant relationship between U.S. market exposure (market beta) of U.S. hedge funds’ long portfolios and subsequent market return.

The high market factor exposure of U.S. hedge funds’ long portfolios is not predictive of subsequent market returns.

The broader group of all 13F filers generated significant negative returns by varying market exposure, particularly during the 2008-2009 volatility.